Maria Hoogstrate

Previous studies indicate that people with intellectual disability are interested in information about their daily life experiences. However, their information behavior has not been examined in relation to their executive function. In this article, we characterize the participants’ health information interests, preferred sources, and search experiences, in relation to their executive function reported through shopping-related activities. A total of 39 participants, aged 19–59 years, who could verbally communicate their daily-life information-seeking experiences were included in the survey. Data was collected using an accessible survey delivered through structured interviews by a researcher familiar with the participants. The quantitative data was used for bar chart creation and cluster analysis. The k -means cluster analysis grouped participants into two distinct clusters; within each cluster, the participant characteristics concerning preferred health information sources, highest educational attainment, and independence in shopping-related functions (functional independence) were found to be homogeneous. Participants who were independent in all four shopping-related functions had vocational training and reported accessing health information from the Internet and chemists. The study highlights that digital independence is linked to the participants’ functional independence. This behavior approach offers novel insights into user profiling, moving beyond traditional IQ-based models, which may guide inclusive technology solutions.

Non-verbal communication is essential for the social inclusion of individuals with an intellectual disability, affecting interactions with others as well as technological systems. This study focuses on non-symbolic communication of people with intellectual disability through generic images without specific or detailed subject matter. A key challenge in this medium is discerning the underlying intentions behind images selected as visual prompts for conversation. Through interviews with people with intellectual disability, we collected a dataset of images and their associated communication intentions, as well as the interpreted intention from potential audiences including humans and systems. Notably, we employed GPT-4, a Large Language Model (LLM), to decipher the images and provide insights from the Conversational Systems’ (CSs) perspective. Adopting a user-centered qualitative approach, we analyzed this data to understand the nuances of image-based self-expression and identify areas of ambiguity. Additionally, we present an analysis of comprehension levels and challenges for intent detection faced by humans and systems. Our findings suggest that generic images offer a rich medium for individuals to share personal interests and unique experiences, enriching communication. Within this framework, we identified that situational, personal, and historical contexts facilitate understanding intents. In a comparative analysis of human and systems viewpoints, we found that LLMs have encouraging capabilities for detecting various aspects of ambiguity in predicting user intentions. Based on this, we offer design strategies for intent clarification and crafting more inclusive multimodal conversational tools for individuals with intellectual disability. Findings can be extrapolated to enhance image-based information retrieval and recommendation systems.